Discussion

Thanks for posting, @ffumarola ! This is earlier than I thought it would be on PH :) (seeing the referrer in google analytics was a surprise)
I started making this 3 months ago because I wanted an easier way for people to get into data science. I specifically decided to use Python because it's a much easier first language to learn (I learned a lot of coding using R, and it can be painful), and it's increasingly being used in production data science work.
I also used the model of "missions" because I wanted context for learning -- instead of learning concepts in isolation, they all tie back to a dataset, and you get to answer questions using real-world data.
It's missing a lot of the more advanced content that I hope to have in the future. I'm working on some stats missions right now.
Let me know if you have any questions/feedback!

I know the maker of this if anyone is interested in asking him questions. His name is Vik Paruchuri. He self-taught himself programming and data science, and has now built this tool to help people start learning data science themselves.
I really enjoy the fact that he lets you try it out before registering. I've gone through a few lessons and I think he did a really great job!

I cannot wait to try this. I'm especially interested in the future courses (stats, machine learning) that you discuss at the bottom of the page. I wonder how this rivals (in terms of content) with Coursera courses.

@bryanpostelnek Awesome! I used to work at edX. The great thing about MOOCs is that they are university courses, for free. They have world-class content and teachers, and comprehensively dive into a subject.
The bad thing is also that they are free university courses. This means that they can be hard to put into sequence (should I take stats 200 before linear algebra 100?), the teaching is more theory-driven, and they are teacher-paced, not self-paced.
The goal with DataQuest is to try to strike a balance. Enough theory to know how things work, with a lot of practical, hands-on problem solving.
As you noted, the content is coming along right now, and there isn't much advanced stuff, but I'm working on some very cool stuff for the next couple months.

Love this. The Data Science specialization on Coursera is fantastic, but uses R, which isn't that helpful if you are trying to build a production ML system. Also, if you're a Python web dev, this is much easier than learning R.
I definitely prefer projects to lectures when I am trying to learn anything technical. I would love to see an updated version of Think Bayes that takes advantage of modern packages. Also, I have never seen a good full featured tutorial on resampling tests using the PyData stack.